---
title: "MongoDBAtlasEmbeddingRetriever"
id: mongodbatlasembeddingretriever
slug: "/mongodbatlasembeddingretriever"
description: "This is an embedding Retriever compatible with the MongoDB Atlas Document Store."
---

# MongoDBAtlasEmbeddingRetriever

This is an embedding Retriever compatible with the MongoDB Atlas Document Store.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | 1. After a Text Embedder and before a [`PromptBuilder`](../builders/promptbuilder.mdx)   in a RAG pipeline 2. The last component in the semantic search pipeline 3. After a Text Embedder and before an [`ExtractiveReader`](../readers/extractivereader.mdx)   in an extractive QA pipeline |
| **Mandatory init variables**           | `document_store`: An instance of a [MongoDBAtlasDocumentStore](../../document-stores/mongodbatlasdocumentstore.mdx)                                                                                                                                                                           |
| **Mandatory run variables**            | `query_embedding`: A list of floats                                                                                                                                                                                                                                       |
| **Output variables**                   | `documents`: A list of documents                                                                                                                                                                                                                                          |
| **API reference**                      | [MongoDB Atlas](/reference/integrations-mongodb-atlas)                                                                                                                                                                                                                           |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mongodb_atlas                                                                                                                                                                           |

</div>

The `MongoDBAtlasEmbeddingRetriever` is an embedding-based Retriever compatible with the [`MongoDBAtlasDocumentStore`](../../document-stores/mongodbatlasdocumentstore.mdx). It compares the query and Document embeddings and fetches the Documents most relevant to the query from the Document Store based on the outcome.

### Parameters

When using the `MongoDBAtlasEmbeddingRetriever` in your NLP system, ensure the query and Document [embeddings](../embedders.mdx) are available. You can do so by adding a Document Embedder to your indexing Pipeline and a Text Embedder to your query Pipeline.

In addition to the `query_embedding`, the `MongoDBAtlasEmbeddingRetriever` accepts other optional parameters, including `top_k` (the maximum number of Documents to retrieve) and `filters` to narrow down the search space.

## Usage

### Installation

To start using MongoDB Atlas with Haystack, install the package with:

```shell
pip install mongodb-atlas-haystack
```

### On its own

The Retriever needs an instance of `MongoDBAtlasDocumentStore` and indexed Documents to run.

```python
from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
from haystack_integrations.components.retrievers.mongodb_atlas import MongoDBAtlasEmbeddingRetriever

document_store = MongoDBAtlasDocumentStore()

retriever = MongoDBAtlasEmbeddingRetriever(document_store=document_store)

## example run query
retriever.run(query_embedding=[0.1]*384)
```

### In a Pipeline

```python
from haystack import Pipeline, Document
from haystack.document_stores.types import DuplicatePolicy
from haystack.components.writers import DocumentWriter
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.embedders import SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder
from haystack_integrations.document_stores.mongodb_atlas import MongoDBAtlasDocumentStore
from haystack_integrations.components.embedders.mongodb_atlas import MongoDBAtlasEmbeddingRetriever

## Create some example documents
documents = [
    Document(content="My name is Jean and I live in Paris."),
    Document(content="My name is Mark and I live in Berlin."),
    Document(content="My name is Giorgio and I live in Rome."),
]

## We support many different databases. Here we load a simple and lightweight in-memory document store.
document_store = MongoDBAtlasDocumentStore()

## Define some more components
doc_writer = DocumentWriter(document_store=document_store, policy=DuplicatePolicy.SKIP)
doc_embedder = SentenceTransformersDocumentEmbedder(model="intfloat/e5-base-v2")
query_embedder = SentenceTransformersTextEmbedder(model="intfloat/e5-base-v2")

## Pipeline that ingests document for retrieval
ingestion_pipe = Pipeline()
ingestion_pipe.add_component(instance=doc_embedder, name="doc_embedder")
ingestion_pipe.add_component(instance=doc_writer, name="doc_writer")

ingestion_pipe.connect("doc_embedder.documents", "doc_writer.documents")
ingestion_pipe.run({"doc_embedder": {"documents": documents}})

## Build a RAG pipeline with a Retriever to get relevant documents to
## the query and a OpenAIGenerator interacting with LLMs using a custom prompt.
prompt_template = """
Given these documents, answer the question.\nDocuments:
{% for doc in documents %}
    {{ doc.content }}
{% endfor %}

\nQuestion: {{question}}
\nAnswer:
"""
rag_pipeline = Pipeline()
rag_pipeline.add_component(instance=query_embedder, name="query_embedder")
rag_pipeline.add_component(instance=MongoDBAtlasEmbeddingRetriever(document_store=document_store), name="retriever")
rag_pipeline.add_component(instance=PromptBuilder(template=prompt_template), name="prompt_builder")
rag_pipeline.add_component(instance=OpenAIGenerator(), name="llm")
rag_pipeline.connect("query_embedder", "retriever.query_embedding")
rag_pipeline.connect("embedding_retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

## Ask a question on the data you just added.
question = "Where does Mark live?"
result = rag_pipeline.run(
    {
        "query_embedder": {"text": question},
        "prompt_builder": {"question": question},
    }
)

## For details, like which documents were used to generate the answer, look into the GeneratedAnswer object
print(result["answer_builder"]["answers"])
```
